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HP HPE2-T38 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Engineering for AI | - Data pipelines and storage considerations - Data preprocessing and feature engineering |
| Artificial Intelligence Fundamentals | - AI use cases in enterprise environments - Types of machine learning (supervised, unsupervised, reinforcement learning) - Core AI concepts and terminology |
| Machine Learning Lifecycle | - Model deployment and monitoring - Data collection and preparation - Model training, validation, and evaluation |
| HPE AI and Machine Learning Solutions | - Hybrid cloud AI infrastructure concepts - HPE Ezmeral for ML operations - HPE GreenLake for AI workloads |
| MLOps and Operationalization | - Monitoring and governance of ML systems - Automation and CI/CD for ML - Model lifecycle management |
HPE AI and Machine Learning Sample Questions:
The HPE Machine Learning Development System helps reduce:
Response:
- A. IT complexity
- B. Legal risks
- C. Marketing costs
- D. Production timelines
Correct Answer: A 🗳️
How can you access documentation for the HPE ML PDK?
Response:
- A. All of the above
- B. Through the HPE website
- C. In the product packaging
- D. By contacting HPE customer support
Correct Answer: A 🗳️
What is the main application of HPE ProLiant Servers in ML?
Response:
- A. IoT device management
- B. Cloud storage optimization
- C. Supporting AI applications like computer vision
- D. Blockchain technology
Correct Answer: C 🗳️
In terms of computational requirements, machine learning solutions generally need:
Response:
- A. Significant computational resources for processing and analysis
- B. The same level of computation as basic web browsing
- C. Minimal computational power
- D. Computational power only for model deployment
Correct Answer: A 🗳️
Which of these is a challenge in ML?
Response:
- A. All of the above
- B. Ethical considerations
- C. Model interpretability
- D. Data acquisition
Correct Answer: A 🗳️
